Semiparametric estimation of a two-component mixture of linear regressions in which one component is known

A new estimation method for the two-component mixture model introduced in \cite{Van13} is proposed. This model consists of a two-component mixture of linear regressions in which one component is entirely known while the proportion, the slope, the intercept and the error distribution of the other component are unknown. In spite of good performance for datasets of reasonable size, the method proposed in \cite{Van13} suffers from a serious drawback when the sample size becomes large as it is based on the optimization of a contrast function whose pointwise computation requires O(n^2) operations. The range of applicability of the method derived in this work is substantially larger as it relies on a method-of-moments estimator free of tuning parameters whose computation requires O(n) operations. From a theoretical perspective, the asymptotic normality of both the estimator of the Euclidean parameter vector and of the semiparametric estimator of the c.d.f.\ of the error is proved under weak conditions not involving zero-symmetry assumptions. In addition, an approximate confidence band for the c.d.f.\ of the error can be computed using a weighted bootstrap whose asymptotic validity is proved. The finite-sample performance of the resulting estimation procedure is studied under various scenarios through Monte Carlo experiments. The proposed method is illustrated on three real datasets of size $n=150$, 51 and 176,343, respectively. Two extensions of the considered model are discussed in the final section: a model with an additional scale parameter for the first component, and a model with more than one explanatory variable.Comment: 43 pages, 4 figures, 5 table

Where are we now with European forest multi-taxon biodiversity and where can we head to?

The European biodiversity and forest strategies rely on forest sustainable management (SFM) to conserve forest biodiversity. However, current sustainability assessments hardly account for direct biodiversity indicators. We focused on forest multi-taxon biodiversity to: i) gather and map the existing information; ii) identify knowledge and research gaps; iii) discuss its research potential. We established a research network to fit data on species, standing trees, lying deadwood and sampling unit description from 34 local datasets across 3591 sampling units. A total of 8724 species were represented, with the share of common and rare species varying across taxonomic classes: some included many species with several rare ones (e.g., Insecta); others (e.g., Bryopsida) were represented by few common species. Tree-related structural attributes were sampled in a subset of sampling units (2889; 2356; 2309 and 1388 respectively for diameter, height, deadwood and microhabitats). Overall, multi-taxon studies are biased towards mature forests and may underrepresent the species related to other developmental phases. European forest compositional categories were all represented, but beech forests were over-represented as compared to thermophilous and boreal forests. Most sampling units (94%) were referred to a habitat type of conservation concern. Existing information may support European conservation and SFM strategies in: (i) methodological harmonization and coordinated monitoring; (ii) definition and testing of SFM indicators and thresholds; (iii) data-driven assessment of the effects of environmental and management drivers on multi-taxon forest biological and functional diversity, (iv) multi-scale forest monitoring integrating in-situ and remotely sensed information

Environmental cues and constraints affecting the seasonality of dominant calanoid copepods in brackish, coastal waters: a case study of Acartia, Temora and Eurytemora species in the south-west Baltic

Information on physiological rates and tolerances helps one gain a cause-and-effect understanding of the role that some environmental (bottom–up) factors play in regulating the seasonality and productivity of key species. We combined the results of laboratory experiments on reproductive success and field time series data on adult abundance to explore factors controlling the seasonality of Acartia spp., Eurytemora affinis and Temora longicornis, key copepods of brackish, coastal and temperate environments. Patterns in laboratory and field data were discussed using a metabolic framework that included the effects of ‘controlling’, ‘masking’ and ‘directive’ environmental factors. Over a 5-year period, changes in adult abundance within two south-west Baltic field sites (Kiel Fjord Pier, 54°19′89N, 10°09′06E, 12–21 psu, and North/Baltic Sea Canal NOK, 54°20′45N, 9°57′02E, 4–10 psu) were evaluated with respect to changes in temperature, salinity, day length and chlorophyll a concentration. Acartia spp. dominated the copepod assemblage at both sites (up to 16,764 and 21,771 females m−3 at NOK and Pier) and was 4 to 10 times more abundant than E. affinis (to 2,939 m−3 at NOK) and T. longicornis (to 1,959 m−3 at Pier), respectively. Species-specific salinity tolerance explains differences in adult abundance between sampling sites whereas phenological differences among species are best explained by the influence of species-specific thermal windows and prey requirements supporting survival and egg production. Multiple intrinsic and extrinsic (environmental) factors influence the production of different egg types (normal and resting), regulate life-history strategies and influence match–mismatch dynamics

Optimized feature fusion of lidar and hyperspectral data for tree species mapping in closed forest canopies

This study deals with data fusion of hyperspectral and LiDAR sensors for tree species mapping in complex, closed forest canopies in Belgium. In particular, seven tree species were mapped: Beech, Ash, Larch, Poplar, Copper beech, Chestnut and Oak. The added value of LiDAR height profile data on tree species mapping was assessed. Sensor data were fused in the PCA domain, while optimal feature combination was derived from the best classification performance (in terms of Kappa and producer's accuracy) based on 5-fold cross-validation. Besides, varying training set sizes were tested (resp. 10%, 30% and 50% number of samples per tree species class). Feature fusion of PCA-transformed HS and LiDAR data was most effective for small sample set sizes reaching a Kappa accuracy improvement of 10.51%